In [1]:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib as mpl
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, places
import xarray as xr
import pandas as pd
%matplotlib inline
Example 1: load a time series at one location by accessing netCDF4 files stored on /results or /results2¶
In [2]:
%%time
start= dt.datetime(2017,3,1)
end=dt.datetime(2017,9,30) # the code called below (evaltools.index_model_files) includes the end date
# in the values returned
basedir='/results/SalishSea/nowcast-green.201812/'
nam_fmt='nowcast'
flen=1 # files contain 1 day of data each
ftype= 'ptrc_T' # load bio files
tres=24 # 1: hourly resolution; 24: daily resolution <- try changing to 1 and loading hourly data
flist=et.index_model_files(start,end,basedir,nam_fmt,flen,ftype,tres)
# flist contains paths: file pathes; t_0 timestemp of start of each file; t_n: timestamp of start of next file
print(flist)
paths t_0 t_n 0 /results/SalishSea/nowcast-green.201812/01mar1... 2017-03-01 2017-03-02 1 /results/SalishSea/nowcast-green.201812/02mar1... 2017-03-02 2017-03-03 2 /results/SalishSea/nowcast-green.201812/03mar1... 2017-03-03 2017-03-04 3 /results/SalishSea/nowcast-green.201812/04mar1... 2017-03-04 2017-03-05 4 /results/SalishSea/nowcast-green.201812/05mar1... 2017-03-05 2017-03-06 .. ... ... ... 209 /results/SalishSea/nowcast-green.201812/26sep1... 2017-09-26 2017-09-27 210 /results/SalishSea/nowcast-green.201812/27sep1... 2017-09-27 2017-09-28 211 /results/SalishSea/nowcast-green.201812/28sep1... 2017-09-28 2017-09-29 212 /results/SalishSea/nowcast-green.201812/29sep1... 2017-09-29 2017-09-30 213 /results/SalishSea/nowcast-green.201812/30sep1... 2017-09-30 2017-10-01 [214 rows x 3 columns] CPU times: user 12 ms, sys: 20 ms, total: 32 ms Wall time: 323 ms
In [3]:
# get model i,j of location S3 from places
ij,ii=places.PLACES['S3']['NEMO grid ji']
ik=0 # choose surface level
In [4]:
bio=xr.open_mfdataset(flist['paths'])
In [5]:
bio
Out[5]:
<xarray.Dataset>
Dimensions: (axis_nbounds: 2, deptht: 40, nvertex: 4, time_counter: 214, x: 398, y: 898)
Coordinates:
nav_lat (y, x) float32 dask.array<chunksize=(898, 398), meta=np.ndarray>
nav_lon (y, x) float32 dask.array<chunksize=(898, 398), meta=np.ndarray>
* deptht (deptht) float32 0.5 1.5 2.5 ... 414.5 441.5
time_centered (time_counter) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
* time_counter (time_counter) datetime64[ns] 2017-03-01T12...
Dimensions without coordinates: axis_nbounds, nvertex, x, y
Data variables:
bounds_lon (time_counter, y, x, nvertex) float32 dask.array<chunksize=(1, 898, 398, 4), meta=np.ndarray>
bounds_lat (time_counter, y, x, nvertex) float32 dask.array<chunksize=(1, 898, 398, 4), meta=np.ndarray>
area (time_counter, y, x) float32 dask.array<chunksize=(1, 898, 398), meta=np.ndarray>
deptht_bounds (time_counter, deptht, axis_nbounds) float32 dask.array<chunksize=(1, 40, 2), meta=np.ndarray>
nitrate (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
ammonium (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
silicon (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
diatoms (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
flagellates (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
ciliates (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
microzooplankton (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
dissolved_organic_nitrogen (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
particulate_organic_nitrogen (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
biogenic_silicon (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
Fraser_tracer (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
mesozooplankton (time_counter, deptht, y, x) float32 dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
Attributes:
name: SalishSea_1d_20170301_20170310
description: biogeochemical variables
title: biogeochemical variables
Conventions: CF-1.6
timeStamp: 2019-Jan-11 10:18:36 GMT
uuid: 8082bc6c-e5cf-46f8-a8ce-8e6f05c20614xarray.Dataset
- axis_nbounds: 2
- deptht: 40
- nvertex: 4
- time_counter: 214
- x: 398
- y: 898
- nav_lat(y, x)float32dask.array<chunksize=(898, 398), meta=np.ndarray>
- standard_name :
- latitude
- long_name :
- Latitude
- units :
- degrees_north
- bounds :
- bounds_lat
Array Chunk Bytes 1.43 MB 1.43 MB Shape (898, 398) (898, 398) Count 1065 Tasks 1 Chunks Type float32 numpy.ndarray - nav_lon(y, x)float32dask.array<chunksize=(898, 398), meta=np.ndarray>
- standard_name :
- longitude
- long_name :
- Longitude
- units :
- degrees_east
- bounds :
- bounds_lon
Array Chunk Bytes 1.43 MB 1.43 MB Shape (898, 398) (898, 398) Count 1065 Tasks 1 Chunks Type float32 numpy.ndarray - deptht(deptht)float320.5 1.5 2.5 ... 387.6 414.5 441.5
- name :
- deptht
- long_name :
- Vertical T levels
- units :
- m
- axis :
- Z
- positive :
- down
- bounds :
- deptht_bounds
array([ 0.5 , 1.500003, 2.500011, 3.500031, 4.500071, 5.500151, 6.50031 , 7.500623, 8.501236, 9.502433, 10.504766, 11.509312, 12.518167, 13.535412, 14.568982, 15.634288, 16.761173, 18.007135, 19.481785, 21.389978, 24.100256, 28.229916, 34.685757, 44.517723, 58.484333, 76.58559 , 98.06296 , 121.866516, 147.08946 , 173.11449 , 199.57304 , 226.2603 , 253.06664 , 279.93454 , 306.8342 , 333.75018 , 360.67453 , 387.6032 , 414.5341 , 441.4661 ], dtype=float32) - time_centered(time_counter)datetime64[ns]dask.array<chunksize=(1,), meta=np.ndarray>
- standard_name :
- time
- long_name :
- Time axis
- time_origin :
- 1900-01-01 00:00:00
- bounds :
- time_centered_bounds
Array Chunk Bytes 1.71 kB 8 B Shape (214,) (1,) Count 642 Tasks 214 Chunks Type datetime64[ns] numpy.ndarray - time_counter(time_counter)datetime64[ns]2017-03-01T12:00:00 ... 2017-09-...
- axis :
- T
- standard_name :
- time
- long_name :
- Time axis
- time_origin :
- 1900-01-01 00:00:00
- bounds :
- time_counter_bounds
array(['2017-03-01T12:00:00.000000000', '2017-03-02T12:00:00.000000000', '2017-03-03T12:00:00.000000000', ..., '2017-09-28T12:00:00.000000000', '2017-09-29T12:00:00.000000000', '2017-09-30T12:00:00.000000000'], dtype='datetime64[ns]')
- bounds_lon(time_counter, y, x, nvertex)float32dask.array<chunksize=(1, 898, 398, 4), meta=np.ndarray>
Array Chunk Bytes 1.22 GB 5.72 MB Shape (214, 898, 398, 4) (1, 898, 398, 4) Count 856 Tasks 214 Chunks Type float32 numpy.ndarray - bounds_lat(time_counter, y, x, nvertex)float32dask.array<chunksize=(1, 898, 398, 4), meta=np.ndarray>
Array Chunk Bytes 1.22 GB 5.72 MB Shape (214, 898, 398, 4) (1, 898, 398, 4) Count 856 Tasks 214 Chunks Type float32 numpy.ndarray - area(time_counter, y, x)float32dask.array<chunksize=(1, 898, 398), meta=np.ndarray>
- standard_name :
- cell_area
- units :
- m2
Array Chunk Bytes 305.94 MB 1.43 MB Shape (214, 898, 398) (1, 898, 398) Count 856 Tasks 214 Chunks Type float32 numpy.ndarray - deptht_bounds(time_counter, deptht, axis_nbounds)float32dask.array<chunksize=(1, 40, 2), meta=np.ndarray>
Array Chunk Bytes 68.48 kB 320 B Shape (214, 40, 2) (1, 40, 2) Count 856 Tasks 214 Chunks Type float32 numpy.ndarray - nitrate(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_nitrate_in_sea_water
- long_name :
- Nitrate Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - time_centered_bounds(time_counter, axis_nbounds)datetime64[ns]dask.array<chunksize=(1, 2), meta=np.ndarray>
Array Chunk Bytes 3.42 kB 16 B Shape (214, 2) (1, 2) Count 642 Tasks 214 Chunks Type datetime64[ns] numpy.ndarray - time_counter_bounds(time_counter, axis_nbounds)datetime64[ns]dask.array<chunksize=(1, 2), meta=np.ndarray>
Array Chunk Bytes 3.42 kB 16 B Shape (214, 2) (1, 2) Count 642 Tasks 214 Chunks Type datetime64[ns] numpy.ndarray - ammonium(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_ammonium_in_sea_water
- long_name :
- Ammonium Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - silicon(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_silicate_in_sea_water
- long_name :
- Silicon Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - diatoms(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_water
- long_name :
- Diatoms Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - flagellates(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_flagellates_expressed_as_nitrogen_in_sea_water
- long_name :
- Flagellates Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - ciliates(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_mesodinium_rubrum_expressed_as_nitrogen_in_sea_water
- long_name :
- Mesodinium rubrum Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - microzooplankton(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_water
- long_name :
- Microzooplankton Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - dissolved_organic_nitrogen(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_water
- long_name :
- Dissolved Organic N Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - particulate_organic_nitrogen(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_particulate_organic_matter_expressed_as_nitrogen_in_sea_water
- long_name :
- Particulate Organic N Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - biogenic_silicon(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_organic_detritus_expressed_as_silicon_in_sea_water
- long_name :
- Biogenic Silicon Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - Fraser_tracer(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- fraser_river_turbidity_tracer
- long_name :
- Fraser River Turbidity Tracer
- units :
- NTU
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray - mesozooplankton(time_counter, deptht, y, x)float32dask.array<chunksize=(1, 40, 898, 398), meta=np.ndarray>
- standard_name :
- mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_water
- long_name :
- Mesozooplankton Concentration
- units :
- mmol m-3
- online_operation :
- average
- interval_operation :
- 40 s
- interval_write :
- 1 d
- cell_methods :
- time: mean (interval: 40 s)
- cell_measures :
- area: area
Array Chunk Bytes 12.24 GB 57.18 MB Shape (214, 40, 898, 398) (1, 40, 898, 398) Count 642 Tasks 214 Chunks Type float32 numpy.ndarray
- name :
- SalishSea_1d_20170301_20170310
- description :
- biogeochemical variables
- title :
- biogeochemical variables
- Conventions :
- CF-1.6
- timeStamp :
- 2019-Jan-11 10:18:36 GMT
- uuid :
- 8082bc6c-e5cf-46f8-a8ce-8e6f05c20614
In [6]:
%%time
tt=bio.time_centered
micZ=bio.microzooplankton.isel(deptht=ik,y=ij,x=ii)
diat=bio.diatoms.isel(deptht=ik,y=ij,x=ii)
CPU times: user 4 ms, sys: 0 ns, total: 4 ms Wall time: 4.58 ms
In [7]:
%%time
fig,ax=plt.subplots(1,1,figsize=(12,3))
ax.plot(tt,diat,'c-',label='diatoms')
ax.plot(tt,micZ,'-',color='darkorange',label='microzooplankton')
ax.legend(loc=2);
ax.set_ylabel('Concentration ($\mu$M N)')
ax.set_xlim(tt[0],tt[-1])
CPU times: user 1min 8s, sys: 11.2 s, total: 1min 19s Wall time: 3min 28s
Out[7]:
(17226.5, 17439.5)
In [ ]:
# Adjust date format display
fig,ax=plt.subplots(1,1,figsize=(12,3))
ax.plot(tt,diat,'c-',label='diatoms')
ax.plot(tt,micZ,'-',color='darkorange',label='microzooplankton')
ax.legend(loc=2);
ax.set_ylabel('Concentration ($\mu$M N)')
ax.set_xlim(tt[0],tt[-1])
yearsFmt = mdates.DateFormatter('%d %b')
ax.xaxis.set_major_formatter(yearsFmt)
ax.set_title('March 2017')
In [13]:
bio.close()
repeat with hourly data over a shorter interval¶
In [11]:
start= dt.datetime(2017,3,1)
end=dt.datetime(2017,3,5) # the code called below (evaltools.index_model_files) includes the end date
# in the values returned
tres=1 # 1: hourly resolution; 24: daily resolution <- try changing to 1 and loading hourly data
flist=et.index_model_files(start,end,basedir,nam_fmt,flen,ftype,tres)
# flist contains paths: file pathes; t_0 timestemp of start of each file; t_n: timestamp of start of next file
flist['paths'][0]
Out[11]:
'/results/SalishSea/nowcast-green.201812/01mar17/SalishSea_1h_20170301_20170301_ptrc_T.nc'
In [12]:
%%time
bio=xr.open_mfdataset(flist['paths'])
tt=bio.time_centered
micZ=bio.microzooplankton.isel(deptht=0,y=ij,x=ii)
diat=bio.diatoms.isel(deptht=0,y=ij,x=ii)
CPU times: user 188 ms, sys: 16 ms, total: 204 ms Wall time: 616 ms
In [13]:
%%time
fig,ax=plt.subplots(1,1,figsize=(12,3))
ax.plot(tt,diat,'c-',label='diatoms')
ax.plot(tt,micZ,'-',color='darkorange',label='microzooplankton')
ax.legend(loc=2);
ax.set_ylabel('Concentration ($\mu$M N)')
ax.set_xlim(tt[0],tt[-1])
CPU times: user 32.7 s, sys: 5.46 s, total: 38.2 s Wall time: 42.2 s
Out[13]:
(17226.020833333332, 17230.979166666668)
In [14]:
bio.close()
Aside: this can also be done using ERDDAP, but only to access 201905 model output¶
In [15]:
%%time
hc1905 = xr.open_dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DBiologyFields1hV19-05')
CPU times: user 24 ms, sys: 4 ms, total: 28 ms Wall time: 57.4 ms
In [16]:
%%time
hc1905
CPU times: user 0 ns, sys: 0 ns, total: 0 ns Wall time: 4.53 µs
Out[16]:
<xarray.Dataset>
Dimensions: (depth: 40, gridX: 398, gridY: 898, time: 123216)
Coordinates:
* time (time) datetime64[ns] 2007-01-01T00:30:00 ....
* depth (depth) float32 0.5 1.5 2.5 ... 414.5 441.5
* gridY (gridY) int16 0 1 2 3 4 ... 894 895 896 897
* gridX (gridX) int16 0 1 2 3 4 ... 394 395 396 397
Data variables:
ammonium (time, depth, gridY, gridX) float32 ...
biogenic_silicon (time, depth, gridY, gridX) float32 ...
ciliates (time, depth, gridY, gridX) float32 ...
diatoms (time, depth, gridY, gridX) float32 ...
dissolved_organic_nitrogen (time, depth, gridY, gridX) float32 ...
flagellates (time, depth, gridY, gridX) float32 ...
mesozooplankton (time, depth, gridY, gridX) float32 ...
microzooplankton (time, depth, gridY, gridX) float32 ...
nitrate (time, depth, gridY, gridX) float32 ...
particulate_organic_nitrogen (time, depth, gridY, gridX) float32 ...
silicon (time, depth, gridY, gridX) float32 ...
Attributes:
acknowledgement: MEOPAR, ONC, Compute Canada
cdm_data_type: Grid
comment: If you use this dataset in your research,\nple...
Conventions: CF-1.6, COARDS, ACDD-1.3
creator_email: sallen@eoas.ubc.ca
creator_name: Salish Sea MEOPAR Project Contributors
creator_url: https://salishsea-meopar-docs.readthedocs.io/
description: biogeochemical variables
drawLandMask: over
history: 2021-01-21T19:06:06Z (local files)\n2021-01-21...
infoUrl: https://salishsea-meopar-docs.readthedocs.io/e...
institution: UBC EOAS
institution_fullname: Earth, Ocean & Atmospheric Sciences, Universit...
keywords: ammonia, ammonium, aquatic, biogenic, biogenic...
keywords_vocabulary: GCMD Science Keywords
license: The Salish Sea MEOPAR NEMO model results are c...
project: Salish Sea MEOPAR NEMO Model
sourceUrl: (local files)
standard_name_vocabulary: CF Standard Name Table v29
summary: Green, Salish Sea, 3d Biology Fields, Hourly, ...
testOutOfDate: now-16hours
time_coverage_end: 2021-01-20T23:30:00Z
time_coverage_start: 2007-01-01T00:30:00Z
timeStamp: 2021-Jan-20 19:28:54 GMT
title: Green, Salish Sea, 3d Biology Fields, Hourly, ...
uuid: 541bbc8f-d5b3-41e7-99b8-be00298750ddxarray.Dataset
- depth: 40
- gridX: 398
- gridY: 898
- time: 123216
- time(time)datetime64[ns]2007-01-01T00:30:00 ... 2021-01-...
- _ChunkSizes :
- 1
- _CoordinateAxisType :
- Time
- actual_range :
- [1.1676114e+09 1.6111854e+09]
- axis :
- T
- comment :
- time values are UTC at the centre of the intervals over which the calculated model results are averaged
- coverage_content_type :
- modelResult
- ioos_category :
- Time
- long_name :
- Time axis
- standard_name :
- time
- time_origin :
- 01-JAN-1970 00:00:00
array(['2007-01-01T00:30:00.000000000', '2007-01-01T01:30:00.000000000', '2007-01-01T02:30:00.000000000', ..., '2021-01-20T21:30:00.000000000', '2021-01-20T22:30:00.000000000', '2021-01-20T23:30:00.000000000'], dtype='datetime64[ns]') - depth(depth)float320.5 1.5 2.5 ... 387.6 414.5 441.5
- _ChunkSizes :
- 40
- _CoordinateAxisType :
- Height
- _CoordinateZisPositive :
- down
- actual_range :
- [ 0.5000003 441.4661 ]
- axis :
- Z
- coverage_content_type :
- modelResult
- ioos_category :
- Location
- long_name :
- Depth
- name :
- deptht
- positive :
- down
- standard_name :
- depth
- units :
- m
array([ 0.5 , 1.500003, 2.500011, 3.500031, 4.500071, 5.500151, 6.50031 , 7.500623, 8.501236, 9.502433, 10.504766, 11.509312, 12.518167, 13.535412, 14.568982, 15.634288, 16.761173, 18.007135, 19.481785, 21.389978, 24.100256, 28.229916, 34.685757, 44.517723, 58.484333, 76.58559 , 98.06296 , 121.866516, 147.08946 , 173.11449 , 199.57304 , 226.2603 , 253.06664 , 279.93454 , 306.8342 , 333.75018 , 360.67453 , 387.6032 , 414.5341 , 441.4661 ], dtype=float32) - gridY(gridY)int160 1 2 3 4 5 ... 893 894 895 896 897
- actual_range :
- [ 0 897]
- coverage_content_type :
- modelResult
- ioos_category :
- location
- long_name :
- Y
- units :
- count
array([ 0, 1, 2, ..., 895, 896, 897], dtype=int16)
- gridX(gridX)int160 1 2 3 4 5 ... 393 394 395 396 397
- actual_range :
- [ 0 397]
- coverage_content_type :
- modelResult
- ioos_category :
- location
- long_name :
- X
- units :
- count
array([ 0, 1, 2, ..., 395, 396, 397], dtype=int16)
- ammonium(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 5.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Ammonium Concentration
- standard_name :
- mole_concentration_of_ammonium_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- biogenic_silicon(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 70.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Biogenic Silicon Concentration
- standard_name :
- mole_concentration_of_organic_detritus_expressed_as_silicon_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- ciliates(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Mesodinium rubrum Concentration
- standard_name :
- mole_concentration_of_mesodinium_rubrum_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- diatoms(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 20.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Diatoms Concentration
- standard_name :
- mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- dissolved_organic_nitrogen(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Dissolved Organic N Concentration
- standard_name :
- mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- flagellates(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 128.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Flagellates Concentration
- standard_name :
- mole_concentration_of_flagellates_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- mesozooplankton(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Mesozooplankton Concentration
- standard_name :
- mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- microzooplankton(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- biology
- long_name :
- Microzooplankton Concentration
- standard_name :
- mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- nitrate(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 40.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Nitrate Concentration
- standard_name :
- mole_concentration_of_nitrate_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- particulate_organic_nitrogen(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Particulate Organic N Concentration
- standard_name :
- mole_concentration_of_particulate_organic_matter_expressed_as_nitrogen_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- silicon(time, depth, gridY, gridX)float32...
- _ChunkSizes :
- [ 1 40 898 398]
- colorBarMaximum :
- 70.0
- colorBarMinimum :
- 0.0
- coverage_content_type :
- modelResult
- ioos_category :
- dissolved_nutrients
- long_name :
- Silicon Concentration
- standard_name :
- mole_concentration_of_silicate_in_sea_water
- units :
- mmol m-3
[1761515650560 values with dtype=float32]
- acknowledgement :
- MEOPAR, ONC, Compute Canada
- cdm_data_type :
- Grid
- comment :
- If you use this dataset in your research, please reference it with wording similar to the example below, and include citations of the publications below. Inclusion of the date(s) when you downloaded the dataset, and the dataset id help to ensure reproducibility of your work. Reference wording: 3d SMELT biological model field values from the SalishSeaCast model (Soontiens et al, 2016; Moore-Maley et al, 2016; Soontiens and Allen, 2017, Olson et al, 2020) were downloaded from their ERDDAP server (https://salishsea.eos.ubc.ca/erddap/) on DATE from dataset ubcSSg3DBiologyFields1hV19-05. Citations: Soontiens, N., Allen, S., Latornell, D., Le Souef, K., Machuca, I., Paquin, J.-P., Lu, Y., Thompson, K., Korabel, V., 2016. Storm surges in the Strait of Georgia simulated with a regional model. Atmosphere-Ocean 54 1-21. https://dx.doi.org/10.1080/07055900.2015.1108899 Moore-Maley, B. L., Allen, S. E., and IansonD., 2016. Locally-driven interannual variability of near-surface pH and ΩA in the Strait of Georgia. J. Geophys. Res. Oceans, 121(3), 1600–1625. https://dx.doi.org/10.1002/2015JC011118 Soontiens, N. and Allen, S., 2017. Modelling sensitivities to mixing and advection in a sill-basin estuarine system. Ocean Modelling, 112, 17-32. https://dx.doi.org/10.1016/j.ocemod.2017.02.008 Olson, E. M., Allen, S. E., Do, Vy, Dunphy, M., and Ianson, D., 2020. Assessment of Nutrient Supply by a Tidal Jet in the Northern Strait of Georgia Based on a Biogeochemical Model. J. Geophys. Res. Oceans, 125(8). https://doi.org/10.1029/2019JC015766
- Conventions :
- CF-1.6, COARDS, ACDD-1.3
- creator_email :
- sallen@eoas.ubc.ca
- creator_name :
- Salish Sea MEOPAR Project Contributors
- creator_url :
- https://salishsea-meopar-docs.readthedocs.io/
- description :
- biogeochemical variables
- drawLandMask :
- over
- history :
- 2021-01-21T19:06:06Z (local files) 2021-01-21T19:06:06Z https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DBiologyFields1hV19-05.das
- infoUrl :
- https://salishsea-meopar-docs.readthedocs.io/en/latest/results_server/index.html#salish-sea-model-results
- institution :
- UBC EOAS
- institution_fullname :
- Earth, Ocean & Atmospheric Sciences, University of British Columbia
- keywords :
- ammonia, ammonium, aquatic, biogenic, biogenic_silicon, biological, biosphere, chemistry, ciliates, concentration, deptht, detritus, diatoms, dissolved, dissolved_organic_nitrogen, Earth Science > Biological Classification > Protists > Diatoms, Earth Science > Biosphere > Aquatic Ecosystems > Plankton > Zooplankton, Earth Science > Oceans > Ocean Chemistry > Ammonia, Earth Science > Oceans > Ocean Chemistry > Nitrate, Earth Science > Oceans > Ocean Chemistry > Nitrogen, Earth Science > Oceans > Ocean Chemistry > Organic Matter, Earth Science > Oceans > Ocean Chemistry > Silicate, ecosystems, flagellates, marine, mesodinium rubrum, mesozooplankton, microzooplankton, mole_concentration_of_ammonium_in_sea_water, mole_concentration_of_diatoms_expressed_as_nitrogen_in_sea_water, mole_concentration_of_flagellates_expressed_as_nitrogen_in_sea_water, mole_concentration_of_mesodinium_rubrum_expressed_as_nitrogen_in_sea_water, mole_concentration_of_mesozooplankton_expressed_as_nitrogen_in_sea_water, mole_concentration_of_microzooplankton_expressed_as_nitrogen_in_sea_water, mole_concentration_of_nitrate_in_sea_water, mole_concentration_of_organic_detritus_expressed_as_nitrogen_in_sea_water, mole_concentration_of_organic_detritus_expressed_as_silicon_in_sea_water, mole_concentration_of_particulate_organic_matter_expressed_as_nitrogen_in_sea_water, mole_concentration_of_silicate_in_sea_water, n02, nh4, nitrate, nitrogen, no3, ocean, oceans, organic, particulate, particulate_organic_nitrogen, plankton, protists, river, sea, seawater, silicate, silicon, time_counter, tracer, water, zooplankton
- keywords_vocabulary :
- GCMD Science Keywords
- license :
- The Salish Sea MEOPAR NEMO model results are copyright by the Salish Sea MEOPAR Project Contributors and The University of British Columbia. They are licensed under the Apache License, Version 2.0. https://www.apache.org/licenses/LICENSE-2.0
- project :
- Salish Sea MEOPAR NEMO Model
- sourceUrl :
- (local files)
- standard_name_vocabulary :
- CF Standard Name Table v29
- summary :
- Green, Salish Sea, 3d Biology Fields, Hourly, v19-05 3d SMELT biological model field values averaged over 1 hour intervals from Salish Sea NEMO model runs with physics, biology and chemistry. The values are calculated for the entire model grid that includes the Juan de Fuca Strait, the Strait of Georgia, Puget Sound, and Johnstone Strait on the coasts of Washington State and British Columbia. The time values are UTC. They are the centre of the intervals over which the calculated model results are averaged. Geo-location and depth data for the Salish Sea NEMO model grid are available in the ubcSSnBathymetry2V17-02 dataset. v17-02: Micromolar concentrations of ammonium, biogenic silicon, ciliates (mesodinium rubrum), diatoms, dissolved organic nitrogen, flagellates (nanophytoplankton), mesozooplankton, microzooplankton, nitrate, particulate organic nitrogen, silicon. Fraser River water turbidity tracer. NEMO-3.6; ubcSSnBathymetryV17-02 bathymetry; see infoUrl link for full details. v18-06: NEMO-3.6 SalishSeaCast-201806 configuration; ubcSSnBathymetryV17-02 bathymetry; see infoUrl link for full details. v18-12: NEMO-3.6 SalishSeaCast-201812 configuration; ubcSSnBathymetryV17-02 bathymetry; see infoUrl link for full details. v19-05: NEMO-3.6 SalishSeaCast-201905 configuration; ubcSSnBathymetryV17-02 bathymetry; see infoUrl link for full details.
- testOutOfDate :
- now-16hours
- time_coverage_end :
- 2021-01-20T23:30:00Z
- time_coverage_start :
- 2007-01-01T00:30:00Z
- timeStamp :
- 2021-Jan-20 19:28:54 GMT
- title :
- Green, Salish Sea, 3d Biology Fields, Hourly, v19-05
- uuid :
- 541bbc8f-d5b3-41e7-99b8-be00298750dd
In [17]:
%%time
modt=hc1905.time.sel(time=slice(start,end))
micZ1905=hc1905.microzooplankton.sel(time=slice(start,end)).isel(depth=ik,gridY=ij,gridX=ii)
diat1905=hc1905.diatoms.sel(time=slice(start,end)).isel(depth=ik,gridY=ij,gridX=ii)
CPU times: user 4 ms, sys: 4 ms, total: 8 ms Wall time: 7.84 ms
In [18]:
%%time
fig,ax=plt.subplots(1,1,figsize=(12,3))
ax.plot(modt,diat1905,'c-',label='diatoms')
ax.plot(modt,micZ1905,'-',color='darkorange',label='microzooplankton')
ax.legend(loc=2);
ax.set_ylabel('Concentration ($\mu$M N)')
ax.set_xlim(modt[0],modt[-1]);
CPU times: user 28 ms, sys: 0 ns, total: 28 ms Wall time: 58.8 s
Out[18]:
(17226.020833333332, 17229.979166666668)
In [19]:
hc1905.close()
back to accessing files from /results and /results2, but now switch to loading 201905 results¶
In [20]:
basedir='/results2/SalishSea/nowcast-green.201905/'
Plot mid Feb -- Jun Surface phytoplankton and nitrate at S3 for 2017:¶
In [21]:
newstart=dt.datetime(2017,2,15)
newend=dt.datetime(2017,6,1)
flist=et.index_model_files(newstart,newend,basedir,'nowcast',1,'ptrc_T',1)
bio2=xr.open_mfdataset(flist['paths'])
ij,ii=places.PLACES['S3']['NEMO grid ji']
ik=0 # choose surface level
In [22]:
%%time
fig,ax=plt.subplots(1,1,figsize=(12,3))
p1=ax.plot(bio2.time_centered,bio2.diatoms.isel(deptht=ik,y=ij,x=ii)+\
bio2.flagellates.isel(deptht=ik,y=ij,x=ii)+bio2.ciliates.isel(deptht=ik,y=ij,x=ii),
'-',color='teal',label='Phytoplankton')
p2=ax.plot(bio2.time_centered,bio2.nitrate.isel(deptht=ik,y=ij,x=ii),
'-',color='orange',label='Nitrate')
ax.legend(handles=[p1[0],p2[0]],loc=1)
ax.set_ylabel('$\mu$M N')
--------------------------------------------------------------------------- IndexError Traceback (most recent call last) ~/anaconda3/envs/py39/lib/python3.9/site-packages/matplotlib/cbook/__init__.py in _check_1d(x) 1317 -> 1318 ndim = x[:, None].ndim 1319 # we have definitely hit a pandas index or series object ~/anaconda3/envs/py39/lib/python3.9/site-packages/xarray/core/dataarray.py in __getitem__(self, key) 700 # xarray-style array indexing --> 701 return self.isel(indexers=self._item_key_to_dict(key)) 702 ~/anaconda3/envs/py39/lib/python3.9/site-packages/xarray/core/dataarray.py in _item_key_to_dict(self, key) 664 else: --> 665 key = indexing.expanded_indexer(key, self.ndim) 666 return dict(zip(self.dims, key)) ~/anaconda3/envs/py39/lib/python3.9/site-packages/xarray/core/indexing.py in expanded_indexer(key, ndim) 47 if len(new_key) > ndim: ---> 48 raise IndexError("too many indices") 49 new_key.extend((ndim - len(new_key)) * [slice(None)]) IndexError: too many indices During handling of the above exception, another exception occurred: KeyboardInterrupt Traceback (most recent call last) <timed exec> in <module> ~/anaconda3/envs/py39/lib/python3.9/site-packages/matplotlib/axes/_axes.py in plot(self, scalex, scaley, data, *args, **kwargs) 1741 """ 1742 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D) -> 1743 lines = [*self._get_lines(*args, data=data, **kwargs)] 1744 for line in lines: 1745 self.add_line(line) ~/anaconda3/envs/py39/lib/python3.9/site-packages/matplotlib/axes/_base.py in __call__(self, data, *args, **kwargs) 271 this += args[0], 272 args = args[1:] --> 273 yield from self._plot_args(this, kwargs) 274 275 def get_next_color(self): ~/anaconda3/envs/py39/lib/python3.9/site-packages/matplotlib/axes/_base.py in _plot_args(self, tup, kwargs) 387 if len(tup) == 2: 388 x = _check_1d(tup[0]) --> 389 y = _check_1d(tup[-1]) 390 else: 391 x, y = index_of(tup[-1]) ~/anaconda3/envs/py39/lib/python3.9/site-packages/matplotlib/cbook/__init__.py in _check_1d(x) 1332 # https://github.com/pandas-dev/pandas/issues/35527 1333 except (AssertionError, IndexError, TypeError): -> 1334 return np.atleast_1d(x) 1335 1336 <__array_function__ internals> in atleast_1d(*args, **kwargs) ~/anaconda3/envs/py39/lib/python3.9/site-packages/numpy/core/shape_base.py in atleast_1d(*arys) 64 res = [] 65 for ary in arys: ---> 66 ary = asanyarray(ary) 67 if ary.ndim == 0: 68 result = ary.reshape(1) ~/anaconda3/envs/py39/lib/python3.9/site-packages/numpy/core/_asarray.py in asanyarray(a, dtype, order) 134 135 """ --> 136 return array(a, dtype, copy=False, order=order, subok=True) 137 138 ~/anaconda3/envs/py39/lib/python3.9/site-packages/xarray/core/common.py in __array__(self, dtype) 129 130 def __array__(self: Any, dtype: DTypeLike = None) -> np.ndarray: --> 131 return np.asarray(self.values, dtype=dtype) 132 133 def __repr__(self) -> str: ~/anaconda3/envs/py39/lib/python3.9/site-packages/xarray/core/dataarray.py in values(self) 626 def values(self) -> np.ndarray: 627 """The array's data as a numpy.ndarray""" --> 628 return self.variable.values 629 630 @values.setter ~/anaconda3/envs/py39/lib/python3.9/site-packages/xarray/core/variable.py in values(self) 510 def values(self): 511 """The variable's data as a numpy.ndarray""" --> 512 return _as_array_or_item(self._data) 513 514 @values.setter ~/anaconda3/envs/py39/lib/python3.9/site-packages/xarray/core/variable.py in _as_array_or_item(data) 272 data = data.get() 273 else: --> 274 data = np.asarray(data) 275 if data.ndim == 0: 276 if data.dtype.kind == "M": ~/anaconda3/envs/py39/lib/python3.9/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order) 81 82 """ ---> 83 return array(a, dtype, copy=False, order=order) 84 85 ~/anaconda3/envs/py39/lib/python3.9/site-packages/dask/array/core.py in __array__(self, dtype, **kwargs) 1414 1415 def __array__(self, dtype=None, **kwargs): -> 1416 x = self.compute() 1417 if dtype and x.dtype != dtype: 1418 x = x.astype(dtype) ~/anaconda3/envs/py39/lib/python3.9/site-packages/dask/base.py in compute(self, **kwargs) 277 dask.base.compute 278 """ --> 279 (result,) = compute(self, traverse=False, **kwargs) 280 return result 281 ~/anaconda3/envs/py39/lib/python3.9/site-packages/dask/base.py in compute(*args, **kwargs) 565 postcomputes.append(x.__dask_postcompute__()) 566 --> 567 results = schedule(dsk, keys, **kwargs) 568 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)]) 569 ~/anaconda3/envs/py39/lib/python3.9/site-packages/dask/threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs) 74 pools[thread][num_workers] = pool 75 ---> 76 results = get_async( 77 pool.apply_async, 78 len(pool._pool), ~/anaconda3/envs/py39/lib/python3.9/site-packages/dask/local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs) 473 # Main loop, wait on tasks to finish, insert new ones 474 while state["waiting"] or state["ready"] or state["running"]: --> 475 key, res_info, failed = queue_get(queue) 476 if failed: 477 exc, tb = loads(res_info) ~/anaconda3/envs/py39/lib/python3.9/site-packages/dask/local.py in queue_get(q) 131 132 def queue_get(q): --> 133 return q.get() 134 135 ~/anaconda3/envs/py39/lib/python3.9/queue.py in get(self, block, timeout) 169 elif timeout is None: 170 while not self._qsize(): --> 171 self.not_empty.wait() 172 elif timeout < 0: 173 raise ValueError("'timeout' must be a non-negative number") ~/anaconda3/envs/py39/lib/python3.9/threading.py in wait(self, timeout) 310 try: # restore state no matter what (e.g., KeyboardInterrupt) 311 if timeout is None: --> 312 waiter.acquire() 313 gotit = True 314 else: KeyboardInterrupt:
Fraser River flow for same times¶
In [23]:
dfFra=pd.read_csv('/ocean/eolson/MEOPAR/obs/ECRivers/Flow/FraserHopeDaily__Dec-2-2020_10_31_05PM.csv',
skiprows=1)
# the original file contains both flow and water level information in the same field (Value)
# keep only the flow data, where PARAM=1 (drop PARAM=2 values)
# flow units are m3/s
dfFra.drop(dfFra.loc[dfFra.PARAM==2].index,inplace=True)
In [25]:
# rename 'Value' column to 'Flow' now that we have removed all the water level rows
dfFra.rename(columns={'Value':'Flow'},inplace=True)
In [26]:
dfFra
Out[26]:
| ID | PARAM | YEAR | DD | Flow | SYM | |
|---|---|---|---|---|---|---|
| 0 | 08MF005 | 1 | 1912 | 61 | 538.0 | NaN |
| 1 | 08MF005 | 1 | 1912 | 62 | 538.0 | NaN |
| 2 | 08MF005 | 1 | 1912 | 63 | 538.0 | NaN |
| 3 | 08MF005 | 1 | 1912 | 64 | 538.0 | NaN |
| 4 | 08MF005 | 1 | 1912 | 65 | 538.0 | NaN |
| ... | ... | ... | ... | ... | ... | ... |
| 39016 | 08MF005 | 1 | 2018 | 360 | 1090.0 | NaN |
| 39017 | 08MF005 | 1 | 2018 | 361 | 1050.0 | NaN |
| 39018 | 08MF005 | 1 | 2018 | 362 | 1030.0 | NaN |
| 39019 | 08MF005 | 1 | 2018 | 363 | 1120.0 | NaN |
| 39020 | 08MF005 | 1 | 2018 | 364 | 1170.0 | NaN |
39021 rows × 6 columns
In [27]:
# no time information so use dt.date
dfFra['Date']=[dt.date(iyr,1,1)+dt.timedelta(days=idd-1) for iyr, idd in zip(dfFra['YEAR'],dfFra['DD'])]
In [28]:
dfFra.head(2)
Out[28]:
| ID | PARAM | YEAR | DD | Flow | SYM | Date | |
|---|---|---|---|---|---|---|---|
| 0 | 08MF005 | 1 | 1912 | 61 | 538.0 | NaN | 1912-03-01 |
| 1 | 08MF005 | 1 | 1912 | 62 | 538.0 | NaN | 1912-03-02 |
In [29]:
# select portion of dataframe in desired date range
dfFra2=dfFra.loc[(dfFra.Date>=newstart.date())&(dfFra.Date<=newend.date())]
In [33]:
fig,ax=plt.subplots(1,1,figsize=(12,3))
ax.plot(dfFra2['Date'],dfFra2['Flow'],'c-')
ax.set_ylabel('Flow (m$^3$s$^{-1}$)')
ax.set_title('Fraser Flow at Hope')
Out[33]:
Text(0.5, 1.0, 'Fraser Flow at Hope')
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